Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/4835
Title: Multi-agent reinforcement learning in stochastic single and multi-stage games
Authors: Verbeeck, Katja
Nowé, Ann
Peeters, Maarten
TUYLS, Karl 
Issue Date: 2005
Publisher: Springer
Source: Adaptive agents and multi-agents systems II. p. 275-294
Series/Report: Lecture Notes in Computer Science
Abstract: In this paper we report on a solution method for one of the most challenging problems in Multi-agent Reinforcement Learning, i.e. coordination. In previous work we reported on a new coordinated exploration technique for individual reinforcement learners, called Exploring Selfish Reinforcement Rearning (ESRL). With this technique, agents may exclude one or more actions from their private action space, so as to coordinate their exploration in a shrinking joint action space. Recently we adapted our solution mechanism to work in tree structured common interest multi-stage games. This paper is a roundup on the results for stochastic single and multi-stage common interest games.
Document URI: http://hdl.handle.net/1942/4835
ISBN: 978-3-540-25260-3
DOI: 10.1007/978-3-540-32274-0_18
ISI #: 000228996700018
Category: C1
Type: Proceedings Paper
Appears in Collections:Research publications

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